log probability
时间: 2023-08-13 12:07:05 浏览: 85
log probability(对数概率)是指某个事件发生的概率的对数值。通常在机器学习和概率统计中使用对数概率来进行计算,因为对数概率具有一些有用的性质,比如可以避免计算机浮点数溢出问题,同时也可以将乘法运算转换为加法运算,简化计算过程。
对数概率的计算公式如下:
log P(x) = log (P1 * P2 * P3 * ... * PN) = log P1 + log P2 + log P3 + ... + log PN
其中,P(x) 是事件发生的概率,P1、P2、P3 等是组成事件的独立事件的概率。
通过使用对数概率,可以更方便地进行概率计算和比较。在机器学习中,常常需要最大化概率或最小化误差,通过将目标函数转化为对数概率后,可以更方便地进行优化和求解。
相关问题
帮我补充代码:class LanguageModel(LanguageModel): def get_unigram_logp(self, unigram): """Computes the log-probability of unigram under this LanguageModel. Args: unigram (str): Unigram for which to compute the log-probability. Returns: log_p (float): Log-probability of unigram under this LanguageModel. """ ### Begin your code ### End your code def get_bigram_logp(self, w_1, w_2): """Computes the log-probability of unigram under this LanguageModel. Note: Use self.lambda_ for the unigram-bigram interpolation factor. Args: w_1 (str): First word in bigram. w_2 (str): Second word in bigram. Returns: log_p (float): Log-probability of bigram under this LanguageModel. """ ### Begin your code ### End your code def get_query_logp(self, query): """Computes the log-probability of query under this LanguageModel. Args: query (str): Whitespace-delimited sequence of terms in the query. Returns: log_p (float): Log-probability assigned to the query under this LanguageModel. """ ### Begin your code ### End your code
在class LanguageModel的基础上,补充完整代码如下:
```
class LanguageModel(LanguageModel):
def get_unigram_logp(self, unigram):
"""Computes the log-probability of unigram under this LanguageModel.
Args:
unigram (str): Unigram for which to compute the log-probability.
Returns:
log_p (float): Log-probability of unigram under this LanguageModel.
"""
count_w1 = self.unigram_counts[unigram]
total_tokens = self.total_num_tokens
V = len(self.unigram_counts)
log_p = np.log((count_w1 + self.lambda_) / (total_tokens + self.lambda_ * V))
return log_p
def get_bigram_logp(self, w_1, w_2):
"""Computes the log-probability of unigram under this LanguageModel.
Note: Use self.lambda_ for the unigram-bigram interpolation factor.
Args:
w_1 (str): First word in bigram.
w_2 (str): Second word in bigram.
Returns:
log_p (float): Log-probability of bigram under this LanguageModel.
"""
count_w1w2 = self.bigram_counts[(w_1, w_2)]
count_w1 = self.unigram_counts[w_1]
total_tokens = self.total_num_tokens
V = len(self.unigram_counts)
log_p = np.log((count_w1w2 + self.lambda_) / (count_w1 + self.lambda_ * V)) + np.log((count_w1 + self.lambda_) / (total_tokens + self.lambda_ * V))
return log_p
def get_query_logp(self, query):
"""Computes the log-probability of query under this LanguageModel.
Args:
query (str): Whitespace-delimited sequence of terms in the query.
Returns:
log_p (float): Log-probability assigned to the query under this LanguageModel.
"""
log_p = 0.0
query_tokens = query.split()
for i, token in enumerate(query_tokens):
if i == 0:
log_p += self.get_unigram_logp(token)
else:
log_p += self.get_bigram_logp(query_tokens[i-1], token)
return log_p
```
其中,get_unigram_logp方法用于计算给定unigram的对数概率,get_bigram_logp方法用于计算给定bigram的对数概率,get_query_logp方法用于计算给定query的对数概率。这些方法都是基于语言模型的先验概率进行计算的。在计算bigram的对数概率时,使用了一元二元平滑的插值方法,其中self.lambda_为插值因子。
'NoneType' object has no attribute 'log_probability'
'NoneType' object has no attribute 'log_probability'是一个常见的错误,通常是由于变量未被正确初始化或者为空值导致的。在Python中,NoneType是一个特殊的数据类型,表示一个空值或者不存在的对象。如果一个变量被赋值为None,那么它就是一个NoneType对象,它没有任何属性或方法,因此在调用它的属性或方法时会出现'NoneType' object has no attribute的错误。
如果你遇到了这个错误,可以检查一下你的代码中是否有变量未被正确初始化或者为空值。你可以使用if语句或者try-except语句来检查变量是否为空,以避免出现这个错误。